{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 1. 导入相关库"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [],
   "source": [
    "import torch\n",
    "import torch.nn as nn\n",
    "from torch.utils.data import Dataset, DataLoader\n",
    "import torchvision.transforms as transforms\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import math\n",
    "\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "\n",
    "# Device configuration\n",
    "device = torch.device('cuda:0' if torch.cuda.is_available() else 'cpu')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 2. 设计数据类"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "dataset = Dataset(normClosePrice,lookback)      \n",
    "dataloader = DataLoader(dataset = dataset, batch_size = batch_size, shuffle=True)   \n",
    "\n",
    "for i, (x,y) in enumerate(dataloader):    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [],
   "source": [
    "class Dataset(Dataset):\n",
    "    \n",
    "    def __init__(self, stock, lookback=20, flag=\"train\"):\n",
    "        data_raw = stock.to_numpy() \n",
    "        data = []\n",
    "\n",
    "        # you can free play（seq_length）\n",
    "        for index in range(len(data_raw) - lookback): \n",
    "            data.append(data_raw[index: index + lookback])\n",
    "\n",
    "        data = np.array(data)\n",
    "        test_set_size = int(np.round(0.2 * data.shape[0]))\n",
    "        train_set_size = data.shape[0] - (test_set_size)\n",
    "        \n",
    "        if flag == \"train\":\n",
    "            self.x = data[:train_set_size,:-1,:]\n",
    "            self.y = data[:train_set_size,-1,0:1]\n",
    "            self.n_samples = len(self.x)\n",
    "        else:\n",
    "            self.x = data[train_set_size:,:-1,:]\n",
    "            self.y = data[train_set_size:,-1,0:1]\n",
    "            self.n_samples = len(self.x)\n",
    "            \n",
    "    \n",
    "    def __getitem__(self,index):\n",
    "        return self.x[index], self.y[index]\n",
    "    \n",
    "    \n",
    "    def __len__(self):\n",
    "        return self.n_samples"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 3. 自定义模型"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [],
   "source": [
    "class NeuralNet(nn.Module):\n",
    "    def __init__(self, input_size, output_size):\n",
    "        super(NeuralNet,self).__init__()\n",
    "        self.l1 = nn.Linear(input_size, 24)\n",
    "        self.bn1 = nn.BatchNorm1d(24)\n",
    "        self.relu = nn.ReLU()\n",
    "        self.l2 = nn.Linear(24,48)\n",
    "        self.bn2 = nn.BatchNorm1d(48)\n",
    "        self.l3 = nn.Linear(48,36)\n",
    "        self.bn3 = nn.BatchNorm1d(36)\n",
    "        self.l4 = nn.Linear(36,8)\n",
    "        self.bn4 = nn.BatchNorm1d(8)\n",
    "        self.l5 = nn.Linear(8,output_size)\n",
    "    \n",
    "    def forward(self, x):\n",
    "        out = self.l1(x)\n",
    "        out = self.bn1(out)\n",
    "        out = self.relu(out)\n",
    "        out = self.l2(out)\n",
    "        out = self.bn2(out)\n",
    "        out = self.relu(out)\n",
    "        out = self.l3(out)\n",
    "        out = self.relu(out)\n",
    "        out = self.l4(out)\n",
    "        out = self.relu(out)\n",
    "        out = self.l5(out)\n",
    "        return out\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 4. 训练过程"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Open</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Close</th>\n",
       "      <th>Adj Close</th>\n",
       "      <th>Volume</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2016-11-02</th>\n",
       "      <td>778.200012</td>\n",
       "      <td>781.650024</td>\n",
       "      <td>763.450012</td>\n",
       "      <td>768.700012</td>\n",
       "      <td>768.700012</td>\n",
       "      <td>1872400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-11-03</th>\n",
       "      <td>767.250000</td>\n",
       "      <td>769.950012</td>\n",
       "      <td>759.030029</td>\n",
       "      <td>762.130005</td>\n",
       "      <td>762.130005</td>\n",
       "      <td>1943200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-11-04</th>\n",
       "      <td>750.659973</td>\n",
       "      <td>770.359985</td>\n",
       "      <td>750.560974</td>\n",
       "      <td>762.020020</td>\n",
       "      <td>762.020020</td>\n",
       "      <td>2134800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-11-07</th>\n",
       "      <td>774.500000</td>\n",
       "      <td>785.190002</td>\n",
       "      <td>772.549988</td>\n",
       "      <td>782.520020</td>\n",
       "      <td>782.520020</td>\n",
       "      <td>1585100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-11-08</th>\n",
       "      <td>783.400024</td>\n",
       "      <td>795.632996</td>\n",
       "      <td>780.190002</td>\n",
       "      <td>790.510010</td>\n",
       "      <td>790.510010</td>\n",
       "      <td>1350800</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  Open        High         Low       Close   Adj Close  \\\n",
       "Date                                                                     \n",
       "2016-11-02  778.200012  781.650024  763.450012  768.700012  768.700012   \n",
       "2016-11-03  767.250000  769.950012  759.030029  762.130005  762.130005   \n",
       "2016-11-04  750.659973  770.359985  750.560974  762.020020  762.020020   \n",
       "2016-11-07  774.500000  785.190002  772.549988  782.520020  782.520020   \n",
       "2016-11-08  783.400024  795.632996  780.190002  790.510010  790.510010   \n",
       "\n",
       "             Volume  \n",
       "Date                 \n",
       "2016-11-02  1872400  \n",
       "2016-11-03  1943200  \n",
       "2016-11-04  2134800  \n",
       "2016-11-07  1585100  \n",
       "2016-11-08  1350800  "
      ]
     },
     "execution_count": 107,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = pd.read_csv('data/rlData.csv', index_col=0)\n",
    "data.head(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "特征工程"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-0.774584</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-0.819985</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-0.820745</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-0.679082</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-0.623868</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          0\n",
       "0 -0.774584\n",
       "1 -0.819985\n",
       "2 -0.820745\n",
       "3 -0.679082\n",
       "4 -0.623868"
      ]
     },
     "execution_count": 108,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "closePrice = data[['Close']]\n",
    "scaler = MinMaxScaler((-1,1)) \n",
    "scaler.fit(closePrice)\n",
    "normClosePrice = scaler.transform(closePrice)\n",
    "normClosePrice = pd.DataFrame(normClosePrice)\n",
    "normClosePrice.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch [10/50], Step [1/3], Loss: 0.30589\n",
      "Epoch [10/50], Step [2/3], Loss: 0.28055\n",
      "Epoch [10/50], Step [3/3], Loss: 0.21083\n",
      "Epoch [20/50], Step [1/3], Loss: 0.19033\n",
      "Epoch [20/50], Step [2/3], Loss: 0.24011\n",
      "Epoch [20/50], Step [3/3], Loss: 0.23441\n",
      "Epoch [30/50], Step [1/3], Loss: 0.12424\n",
      "Epoch [30/50], Step [2/3], Loss: 0.14898\n",
      "Epoch [30/50], Step [3/3], Loss: 0.10656\n",
      "Epoch [40/50], Step [1/3], Loss: 0.03384\n",
      "Epoch [40/50], Step [2/3], Loss: 0.02580\n",
      "Epoch [40/50], Step [3/3], Loss: 0.01689\n",
      "Epoch [50/50], Step [1/3], Loss: 0.00849\n",
      "Epoch [50/50], Step [2/3], Loss: 0.00303\n",
      "Epoch [50/50], Step [3/3], Loss: 0.00768\n"
     ]
    }
   ],
   "source": [
    "# 超参数\n",
    "num_epochs = 50\n",
    "batch_size = 64\n",
    "learning_rate = 0.0005\n",
    "lookback = 20\n",
    "output_size = 1\n",
    "\n",
    "\n",
    "# Load data\n",
    "dataset = Dataset(normClosePrice,lookback)\n",
    "dataloader = DataLoader(dataset = dataset, batch_size = batch_size, \n",
    "                        shuffle=True)\n",
    "\n",
    "input_size = lookback - 1\n",
    "model = NeuralNet(input_size, output_size)\n",
    "# loss\n",
    "criterion = nn.MSELoss()\n",
    "# optimizer\n",
    "optimizer = torch.optim.Adam(model.parameters(), lr=learning_rate)\n",
    "\n",
    "# training loop\n",
    "n_total_steps = len(dataloader)\n",
    "for epoch in range(num_epochs):\n",
    "    model.train()\n",
    "\n",
    "    for i, (x,y) in enumerate(dataloader):\n",
    "        x = x.reshape(x.shape[0], -1).float()\n",
    "        y = y.float()\n",
    "        y_pred = model(x)\n",
    "        loss = criterion(y_pred, y)\n",
    "        optimizer.zero_grad()\n",
    "        loss.backward()\n",
    "        optimizer.step() \n",
    "\n",
    "        if (epoch+1) % 10 ==0: \n",
    "            if (i+1) % 1 == 0:\n",
    "                print(f'Epoch [{epoch+1}/{num_epochs}], Step [{i+1}/{n_total_steps}], Loss: {loss.item():.5f}')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "模型保存"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {},
   "outputs": [],
   "source": [
    "FILE = f'./model_ANN.pth'\n",
    "torch.save(model, FILE)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 5. Inference"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {},
   "outputs": [],
   "source": [
    "dataset = Dataset(normClosePrice,lookback, \"test\")\n",
    "input_x = dataset.x\n",
    "input_x =input_x.reshape(-1, 19)\n",
    "y_test_pred = model(torch.tensor(input_x).float())\n",
    "y_test_pred = scaler.inverse_transform(y_test_pred.detach().numpy())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(46, 1)"
      ]
     },
     "execution_count": 112,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_test = dataset.y\n",
    "y_test = scaler.inverse_transform(y_test)\n",
    "y_test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Model ANN')"
      ]
     },
     "execution_count": 113,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1200x800 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Visualize\n",
    "fig = plt.figure(figsize=(6,4), dpi=200)\n",
    "plt.plot(y_test, label = 'actual')\n",
    "plt.plot(y_test_pred, label = 'prediction')\n",
    "\n",
    "plt.legend(loc=1)\n",
    "plt.ylabel('Price')\n",
    "plt.title('Model ANN')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.13"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
